3D shape generation aims to produce innovative 3D content adhering to specific conditions and constraints. Existing methods often decompose 3D shapes into a sequence of localized components, treating each element in isolation without considering spatial consistency. As a result, these approaches exhibit limited versatility in 3D data representation and shape generation, hindering their ability to generate highly diverse 3D shapes that comply with the specified constraints. In this paper, we introduce a novel spatial-aware 3D shape generation framework that leverages 2D plane representations for enhanced 3D shape modeling. To ensure spatial coherence and reduce memory usage, we incorporate a hybrid shape representation technique that directly learns a continuous signed distance field representation of the 3D shape using orthogonal 2D planes. Additionally, we meticulously enforce spatial correspondences across distinct planes using a transformer-based autoencoder structure, promoting the preservation of spatial relationships in the generated 3D shapes. This yields an algorithm that consistently outperforms state-of-the-art 3D shape generation methods on various tasks, including unconditional shape generation, multi-modal shape completion, single-view reconstruction, and text-to-shape synthesis.

翻译：三维形状生成旨在根据特定条件和约束创作新颖的三维内容。现有方法常将三维形状分解为局部组件序列，孤立处理各组件而未考虑空间一致性。因此，这些方法在三维数据表示与形状生成方面灵活性有限，难以生成高度多样化且符合指定约束的三维形状。本文提出一种新颖的空间感知三维形状生成框架，利用二维平面表示增强三维形状建模。为确保空间连贯性并降低内存消耗，我们引入混合形状表示技术，通过正交二维平面直接学习三维形状的连续符号距离场表示。此外，我们利用基于Transformer的自编码器结构严格强化不同平面间的空间对应关系，从而促进生成三维形状中空间关系的保持。该算法在无条件形状生成、多模态形状补全、单视图重建及文本驱动形状合成等多项任务中，持续优于最先进的三维形状生成方法。